The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Comparative Assessment of Scoring Functions on an Updated Benchmark: 1. Compilation of the Test Set
Abstract
Scoring functions are often applied in combination with molecular docking methods to predict ligand binding poses and ligand binding affinities or to identify active compounds through virtual screening. An objective benchmark for assessing the performance of current scoring functions is expected to provide practical guidance for the users to make smart choices among available methods. It can also elucidate the common weakness in current methods for future improvements. The primary goal of our comparative assessment of scoring functions (CASF) project is to provide a high-standard, publicly accessible benchmark of this type. Our latest study, i.e., CASF-2013, evaluated 20 popular scoring functions on an updated set of protein-ligand complexes. This data set was selected out of 8302 protein-ligand complexes recorded in the PDBbind database (version 2013) through a fairly complicated process. Sample selection was made by considering the quality of complex structures as well as binding data. Finally, qualified complexes were clustered by 90% similarity in protein sequences. Three representative complexes were chosen from each cluster to control sample redundancy. The final outcome, namely, the PDBbind core set (version 2013), consists of 195 protein-ligand complexes in 65 clusters with binding constants spanning nearly 10 orders of magnitude. In this data set, 82% of the ligand molecules are "druglike" and 78% of the protein molecules are validated or potential drug targets. Correlation between binding constants and several key properties of ligands are discussed. Methods and results of the scoring function evaluation will be described in a companion work in this issue (doi: 10.1021/ci500081m ).
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The PDBbind database: methodologies and updates
Wang, R., Fang, X., Lu, Y., Yang, C.-Y., & Wang, S. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm048957q · Cited by 933
Self-citationComparative evaluation of 11 scoring functions for molecular docking.
Wang, R., Lu, Y., & Wang, S. (2003) · Journal of Medicinal Chemistry · doi:10.1021/jm0203783 · Cited by 849
Self-citationBissantz, C., Folkers, G., & Rognan, D. (2000) · Journal of Medicinal Chemistry · doi:10.1021/jm001044l · Cited by 740
Comparative evaluation of eight docking tools for docking and virtual screening accuracy
Kellenberger, E., Rodrigo, J., Muller, P., & Rognan, D. (2004) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.20149 · Cited by 577
Binding MOAD (Mother Of All Databases)
Hu, L., Benson, M. L., Smith, R. D., Lerner, M. G., & Carlson, H. A. (2005) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.20512 · Cited by 328
Ligand efficiency and fragment-based drug discovery
Bembenek, S. D., Tounge, B. A., & Reynolds, C. H. (2009) · Drug Discovery Today · doi:10.1016/j.drudis.2008.11.007 · Cited by 196
Damm-Ganamet, K. L., Smith, R. D., Dunbar, J. B., Stuckey, J. A., & Carlson, H. A. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400025f · Cited by 133
Cited by
Wang, Z., Sun, H., Yao, X., Li, D., Xu, L., Li, Y., Tian, S., & Hou, T. (2016) · Physical Chemistry Chemical Physics · doi:10.1039/c6cp01555g · Cited by 946
Self-citationPolypharmacology by Design: A Medicinal Chemist’s Perspective on Multitargeting Compounds
Proschak, E., Stark, H., & Merk, D. (2019) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.8b00760 · Cited by 498
Forging the basis for developing protein-ligand interaction scoring functions.
Liu, Z., Su, M., Han, L., Liu, J., Yang, Q., Li, Y., & Wang, R. (2017) · Accounts of Chemical Research · doi:10.1021/acs.accounts.6b00491 · Cited by 493
Self-citationEvaluation of AutoDock and AutoDock Vina on the CASF-2013 Benchmark
Gaillard, T. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00312 · Cited by 473
Duan, L., Liu, X., & Zhang, J. Z. H. (2016) · Journal of the American Chemical Society · doi:10.1021/jacs.6b02682 · Cited by 465
Metadata
- Authors
- Li, Y., Liu, Z., Li, J., Han, L., Liu, J., Zhao, Z., & Wang, R.
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2014-06-02
- Volume
- 54
- Issue
- 6
- Pages
- 1700-1716
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W1969017960
- Cited by
- 215
- References
- 7
- FWCI
- 17.75
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.